Paper Info
Title
Masquerade Detection Using Command Prediction and Association Rules Mining
Abstract
Masqueraders commonly impersonate legitimate userpsilas account to gain access to computer systems that they are not authorized to enter. Normally users exhibit some regularity in their behavior such as command usage. We propose a new approach to mine user command associations. Since each user may have different usage behavior, using the built behavior pattern to predict a masqueraderpsilas next command will result in low success rate. We devise an algorithm to identify masqueraders by evaluating the accuracy of the predictions. Furthermore our detection method can be used in real-time without having to wait for a log of a large number of commands. Experimental results show that the association rules mining performs very well in detecting masqueraders.
Year
DOI
Venue
2009
10.1109/AINA.2009.38
AINA
Keywords
Field
DocType
masqueraders,impersonate legitimate user,user command association,data privacy,different usage behavior,behavior pattern,command prediction,computer system,detection method,next command,association rules mining,intrusion detection approaches,computer systems,network security,masquerade detection,data mining,intrusion detection,association rule mining,security of data,command usage,real time systems,association rules,real time,computer networks,algorithm design and analysis,testing,frequency,application software,computer science,prediction algorithms,accuracy,computer security
Behavioral pattern,Data mining,Algorithm design,Computer science,Network security,Association rule learning,Prediction algorithms,Information privacy,Application software,Intrusion detection system
Conference
ISSN
ISBN
Citations 
1550-445X E-ISBN : 978-0-7695-3638-5
978-0-7695-3638-5
2
PageRank 
References 
Authors
0.40
15
2
Name
Order
Citations
PageRank
Han-Ching Wu1222.85
Shou-hsuan Stephen Huang217459.88